Extracting and Visualizing Stock Data
Description
Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.
Table of Contents
- Define a Function that Makes a Graph
- Question 1: Use yfinance to Extract Stock Data
- Question 2: Use Webscraping to Extract Tesla Revenue Data
- Question 3: Use yfinance to Extract Stock Data
- Question 4: Use Webscraping to Extract GME Revenue Data
- Question 5: Plot Tesla Stock Graph
- Question 6: Plot GameStop Stock Graph
Estimated Time Needed: 30 min
Note:- If you are working Locally using anaconda, please uncomment the following code and execute it. Use the version as per your python version.
In [1]:
!pip install yfinance
!pip install bs4
!pip install nbformat
!pip install --upgrade plotly
Requirement already satisfied: yfinance in /opt/conda/lib/python3.12/site-packages (0.2.55) Requirement already satisfied: pandas>=1.3.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.2.3) Requirement already satisfied: numpy>=1.16.5 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.2.5) Requirement already satisfied: requests>=2.31 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.32.3) Requirement already satisfied: multitasking>=0.0.7 in /opt/conda/lib/python3.12/site-packages (from yfinance) (0.0.11) Requirement already satisfied: platformdirs>=2.0.0 in /opt/conda/lib/python3.12/site-packages (from yfinance) (4.3.6) Requirement already satisfied: pytz>=2022.5 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2024.2) Requirement already satisfied: frozendict>=2.3.4 in /opt/conda/lib/python3.12/site-packages (from yfinance) (2.4.6) Requirement already satisfied: peewee>=3.16.2 in /opt/conda/lib/python3.12/site-packages (from yfinance) (3.17.9) Requirement already satisfied: beautifulsoup4>=4.11.1 in /opt/conda/lib/python3.12/site-packages (from yfinance) (4.12.3) Requirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.12/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.5) Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.12/site-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0) Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.12/site-packages (from pandas>=1.3.0->yfinance) (2025.2) Requirement already satisfied: charset_normalizer<4,>=2 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (3.4.1) Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (3.10) Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (2.3.0) Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.12/site-packages (from requests>=2.31->yfinance) (2024.12.14) Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=1.3.0->yfinance) (1.17.0) Requirement already satisfied: bs4 in /opt/conda/lib/python3.12/site-packages (0.0.2) Requirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.12/site-packages (from bs4) (4.12.3) Requirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.12/site-packages (from beautifulsoup4->bs4) (2.5) Requirement already satisfied: nbformat in /opt/conda/lib/python3.12/site-packages (5.10.4) Requirement already satisfied: fastjsonschema>=2.15 in /opt/conda/lib/python3.12/site-packages (from nbformat) (2.21.1) Requirement already satisfied: jsonschema>=2.6 in /opt/conda/lib/python3.12/site-packages (from nbformat) (4.23.0) Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /opt/conda/lib/python3.12/site-packages (from nbformat) (5.7.2) Requirement already satisfied: traitlets>=5.1 in /opt/conda/lib/python3.12/site-packages (from nbformat) (5.14.3) Requirement already satisfied: attrs>=22.2.0 in /opt/conda/lib/python3.12/site-packages (from jsonschema>=2.6->nbformat) (25.1.0) Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /opt/conda/lib/python3.12/site-packages (from jsonschema>=2.6->nbformat) (2024.10.1) Requirement already satisfied: referencing>=0.28.4 in /opt/conda/lib/python3.12/site-packages (from jsonschema>=2.6->nbformat) (0.36.2) Requirement already satisfied: rpds-py>=0.7.1 in /opt/conda/lib/python3.12/site-packages (from jsonschema>=2.6->nbformat) (0.22.3) Requirement already satisfied: platformdirs>=2.5 in /opt/conda/lib/python3.12/site-packages (from jupyter-core!=5.0.*,>=4.12->nbformat) (4.3.6) Requirement already satisfied: typing-extensions>=4.4.0 in /opt/conda/lib/python3.12/site-packages (from referencing>=0.28.4->jsonschema>=2.6->nbformat) (4.12.2) Requirement already satisfied: plotly in /opt/conda/lib/python3.12/site-packages (6.0.1) Requirement already satisfied: narwhals>=1.15.1 in /opt/conda/lib/python3.12/site-packages (from plotly) (1.35.0) Requirement already satisfied: packaging in /opt/conda/lib/python3.12/site-packages (from plotly) (24.2)
In [46]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [47]:
import plotly.io as pio
pio.renderers.default = "iframe"
In Python, you can ignore warnings using the warnings module. You can use the filterwarnings function to filter or ignore specific warning messages or categories.
In [48]:
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore", category=FutureWarning)
Define Graphing Function¶
In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.
In [49]:
def make_graph(stock_data, revenue_data, stock):
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
fig.update_xaxes(title_text="Date", row=1, col=1)
fig.update_xaxes(title_text="Date", row=2, col=1)
fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
fig.update_layout(showlegend=False,
height=900,
title=stock,
xaxis_rangeslider_visible=True)
fig.show()
from IPython.display import display, HTML
fig_html = fig.to_html()
display(HTML(fig_html))
Use the make_graph function that we’ve already defined. You’ll need to invoke it in questions 5 and 6 to display the graphs and create the dashboard.
Note: You don’t need to redefine the function for plotting graphs anywhere else in this notebook; just use the existing function.
Question 1: Use yfinance to Extract Stock Data¶
Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.
In [56]:
tsla=yf.Ticker("TSLA")
Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to "max" so we get information for the maximum amount of time.
In [57]:
tesla_data=tsla.history(period='max')
print(tesla_data.head())
Open High Low Close Volume \
Date
2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 281494500
2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 257806500
2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 123282000
2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 77097000
2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000 103003500
Dividends Stock Splits
Date
2010-06-29 00:00:00-04:00 0.0 0.0
2010-06-30 00:00:00-04:00 0.0 0.0
2010-07-01 00:00:00-04:00 0.0 0.0
2010-07-02 00:00:00-04:00 0.0 0.0
2010-07-06 00:00:00-04:00 0.0 0.0
Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.
In [58]:
tesla_data.reset_index(inplace=True)
print(tesla_data.head())
Date Open High Low Close \
0 2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667
1 2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667
2 2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000
3 2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000
4 2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000
Volume Dividends Stock Splits
0 281494500 0.0 0.0
1 257806500 0.0 0.0
2 123282000 0.0 0.0
3 77097000 0.0 0.0
4 103003500 0.0 0.0
In [ ]:
Question 2: Use Webscraping to Extract Tesla Revenue Data¶
Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named html_data.
In [59]:
url="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
response=requests.get(url)
html_data=response.text
Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.
In [60]:
soup=BeautifulSoup(html_data,'html.parser')
Using BeautifulSoup or the read_html function extract the table with Tesla Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.
Step-by-step instructions
Here are the step-by-step instructions:
1. Create an Empty DataFrame
2. Find the Relevant Table
3. Check for the Tesla Quarterly Revenue Table
4. Iterate Through Rows in the Table Body
5. Extract Data from Columns
6. Append Data to the DataFrame
Click here if you need help locating the table
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
soup.find_all("tbody")[1]
If you want to use the read_html function the table is located at index 1
We are focusing on quarterly revenue in the lab.
In [61]:
tesla_revenue=pd.DataFrame(columns=['Date','Revenue'])
table=soup.find_all('tbody')[1]
print(table)
<tbody> <tr> <td style="text-align:center">2022-09-30</td> <td style="text-align:center">$21,454</td> </tr> <tr> <td style="text-align:center">2022-06-30</td> <td style="text-align:center">$16,934</td> </tr> <tr> <td style="text-align:center">2022-03-31</td> <td style="text-align:center">$18,756</td> </tr> <tr> <td style="text-align:center">2021-12-31</td> <td style="text-align:center">$17,719</td> </tr> <tr> <td style="text-align:center">2021-09-30</td> <td style="text-align:center">$13,757</td> </tr> <tr> <td style="text-align:center">2021-06-30</td> <td style="text-align:center">$11,958</td> </tr> <tr> <td style="text-align:center">2021-03-31</td> <td style="text-align:center">$10,389</td> </tr> <tr> <td style="text-align:center">2020-12-31</td> <td style="text-align:center">$10,744</td> </tr> <tr> <td style="text-align:center">2020-09-30</td> <td style="text-align:center">$8,771</td> </tr> <tr> <td style="text-align:center">2020-06-30</td> <td style="text-align:center">$6,036</td> </tr> <tr> <td style="text-align:center">2020-03-31</td> <td style="text-align:center">$5,985</td> </tr> <tr> <td style="text-align:center">2019-12-31</td> <td style="text-align:center">$7,384</td> </tr> <tr> <td style="text-align:center">2019-09-30</td> <td style="text-align:center">$6,303</td> </tr> <tr> <td style="text-align:center">2019-06-30</td> <td style="text-align:center">$6,350</td> </tr> <tr> <td style="text-align:center">2019-03-31</td> <td style="text-align:center">$4,541</td> </tr> <tr> <td style="text-align:center">2018-12-31</td> <td style="text-align:center">$7,226</td> </tr> <tr> <td style="text-align:center">2018-09-30</td> <td style="text-align:center">$6,824</td> </tr> <tr> <td style="text-align:center">2018-06-30</td> <td style="text-align:center">$4,002</td> </tr> <tr> <td style="text-align:center">2018-03-31</td> <td style="text-align:center">$3,409</td> </tr> <tr> <td style="text-align:center">2017-12-31</td> <td style="text-align:center">$3,288</td> </tr> <tr> <td style="text-align:center">2017-09-30</td> <td style="text-align:center">$2,985</td> </tr> <tr> <td style="text-align:center">2017-06-30</td> <td style="text-align:center">$2,790</td> </tr> <tr> <td style="text-align:center">2017-03-31</td> <td style="text-align:center">$2,696</td> </tr> <tr> <td style="text-align:center">2016-12-31</td> <td style="text-align:center">$2,285</td> </tr> <tr> <td style="text-align:center">2016-09-30</td> <td style="text-align:center">$2,298</td> </tr> <tr> <td style="text-align:center">2016-06-30</td> <td style="text-align:center">$1,270</td> </tr> <tr> <td style="text-align:center">2016-03-31</td> <td style="text-align:center">$1,147</td> </tr> <tr> <td style="text-align:center">2015-12-31</td> <td style="text-align:center">$1,214</td> </tr> <tr> <td style="text-align:center">2015-09-30</td> <td style="text-align:center">$937</td> </tr> <tr> <td style="text-align:center">2015-06-30</td> <td style="text-align:center">$955</td> </tr> <tr> <td style="text-align:center">2015-03-31</td> <td style="text-align:center">$940</td> </tr> <tr> <td style="text-align:center">2014-12-31</td> <td style="text-align:center">$957</td> </tr> <tr> <td style="text-align:center">2014-09-30</td> <td style="text-align:center">$852</td> </tr> <tr> <td style="text-align:center">2014-06-30</td> <td style="text-align:center">$769</td> </tr> <tr> <td style="text-align:center">2014-03-31</td> <td style="text-align:center">$621</td> </tr> <tr> <td style="text-align:center">2013-12-31</td> <td style="text-align:center">$615</td> </tr> <tr> <td style="text-align:center">2013-09-30</td> <td style="text-align:center">$431</td> </tr> <tr> <td style="text-align:center">2013-06-30</td> <td style="text-align:center">$405</td> </tr> <tr> <td style="text-align:center">2013-03-31</td> <td style="text-align:center">$562</td> </tr> <tr> <td style="text-align:center">2012-12-31</td> <td style="text-align:center">$306</td> </tr> <tr> <td style="text-align:center">2012-09-30</td> <td style="text-align:center">$50</td> </tr> <tr> <td style="text-align:center">2012-06-30</td> <td style="text-align:center">$27</td> </tr> <tr> <td style="text-align:center">2012-03-31</td> <td style="text-align:center">$30</td> </tr> <tr> <td style="text-align:center">2011-12-31</td> <td style="text-align:center">$39</td> </tr> <tr> <td style="text-align:center">2011-09-30</td> <td style="text-align:center">$58</td> </tr> <tr> <td style="text-align:center">2011-06-30</td> <td style="text-align:center">$58</td> </tr> <tr> <td style="text-align:center">2011-03-31</td> <td style="text-align:center">$49</td> </tr> <tr> <td style="text-align:center">2010-12-31</td> <td style="text-align:center">$36</td> </tr> <tr> <td style="text-align:center">2010-09-30</td> <td style="text-align:center">$31</td> </tr> <tr> <td style="text-align:center">2010-06-30</td> <td style="text-align:center">$28</td> </tr> <tr> <td style="text-align:center">2010-03-31</td> <td style="text-align:center">$21</td> </tr> <tr> <td style="text-align:center">2009-12-31</td> <td style="text-align:center"></td> </tr> <tr> <td style="text-align:center">2009-09-30</td> <td style="text-align:center">$46</td> </tr> <tr> <td style="text-align:center">2009-06-30</td> <td style="text-align:center">$27</td> </tr> </tbody>
In [63]:
for row in table.find_all('tr'):
columns = row.find_all('td')
if len(columns) > 0:
date = columns[0].text.strip()
revenue = columns[1].text.strip()
new_row = pd.DataFrame({'Date': [date], 'Revenue': [revenue]})
tesla_revenue = pd.concat([tesla_revenue, new_row], ignore_index=True)
tesla_revenue['Revenue'] = tesla_revenue['Revenue'].replace('[\$,]', '', regex=True)
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'].str.strip() != '']
tesla_revenue = tesla_revenue.dropna(subset=['Revenue'])
tesla_revenue['Revenue'] = tesla_revenue['Revenue'].astype(float)
print(tesla_revenue.head())
Date Revenue 0 2022-09-30 21454.0 1 2022-06-30 16934.0 2 2022-03-31 18756.0 3 2021-12-31 17719.0 4 2021-09-30 13757.0
Execute the following line to remove the comma and dollar sign from the Revenue column.
In [53]:
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"")
Execute the following lines to remove an null or empty strings in the Revenue column.
In [54]:
tesla_revenue.dropna(inplace=True)
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
tesla_revenue.shape
Out[54]:
(1, 2)
Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.
In [55]:
tesla_revenue.tail()
Out[55]:
| Date | Revenue | |
|---|---|---|
| 0 | 2005-01-31 | $709 |
Question 3: Use yfinance to Extract Stock Data¶
Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.
In [67]:
gme=yf.Ticker("GME")
Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to "max" so we get information for the maximum amount of time.
In [68]:
gme_data=gme.history(period='max')
Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.
In [69]:
gme_data.reset_index(inplace=True)
gme_data.head()
Out[69]:
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2002-02-13 00:00:00-05:00 | 1.620129 | 1.693350 | 1.603296 | 1.691667 | 76216000 | 0.0 | 0.0 |
| 1 | 2002-02-14 00:00:00-05:00 | 1.712707 | 1.716074 | 1.670626 | 1.683251 | 11021600 | 0.0 | 0.0 |
| 2 | 2002-02-15 00:00:00-05:00 | 1.683251 | 1.687459 | 1.658002 | 1.674834 | 8389600 | 0.0 | 0.0 |
| 3 | 2002-02-19 00:00:00-05:00 | 1.666418 | 1.666418 | 1.578047 | 1.607504 | 7410400 | 0.0 | 0.0 |
| 4 | 2002-02-20 00:00:00-05:00 | 1.615920 | 1.662210 | 1.603296 | 1.662210 | 6892800 | 0.0 | 0.0 |
Question 4: Use Webscraping to Extract GME Revenue Data¶
Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data_2.
In [70]:
url="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
response=requests.get(url)
html_data_2=response.text
Parse the html data using beautiful_soup using parser i.e html5lib or html.parser.
In [71]:
soup1=BeautifulSoup(html_data_2,'html.parser')
Using BeautifulSoup or the read_html function extract the table with GameStop Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column.
Note: Use the method similar to what you did in question 2.
Click here if you need help locating the table
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
soup.find_all("tbody")[1]
If you want to use the read_html function the table is located at index 1
In [72]:
gme_revenue=pd.DataFrame(columns=['Date','Revenue'])
table=soup1.find_all('tbody')[1]
print(table)
<tbody> <tr> <td style="text-align:center">2020-04-30</td> <td style="text-align:center">$1,021</td> </tr> <tr> <td style="text-align:center">2020-01-31</td> <td style="text-align:center">$2,194</td> </tr> <tr> <td style="text-align:center">2019-10-31</td> <td style="text-align:center">$1,439</td> </tr> <tr> <td style="text-align:center">2019-07-31</td> <td style="text-align:center">$1,286</td> </tr> <tr> <td style="text-align:center">2019-04-30</td> <td style="text-align:center">$1,548</td> </tr> <tr> <td style="text-align:center">2019-01-31</td> <td style="text-align:center">$3,063</td> </tr> <tr> <td style="text-align:center">2018-10-31</td> <td style="text-align:center">$1,935</td> </tr> <tr> <td style="text-align:center">2018-07-31</td> <td style="text-align:center">$1,501</td> </tr> <tr> <td style="text-align:center">2018-04-30</td> <td style="text-align:center">$1,786</td> </tr> <tr> <td style="text-align:center">2018-01-31</td> <td style="text-align:center">$2,825</td> </tr> <tr> <td style="text-align:center">2017-10-31</td> <td style="text-align:center">$1,989</td> </tr> <tr> <td style="text-align:center">2017-07-31</td> <td style="text-align:center">$1,688</td> </tr> <tr> <td style="text-align:center">2017-04-30</td> <td style="text-align:center">$2,046</td> </tr> <tr> <td style="text-align:center">2017-01-31</td> <td style="text-align:center">$2,403</td> </tr> <tr> <td style="text-align:center">2016-10-31</td> <td style="text-align:center">$1,959</td> </tr> <tr> <td style="text-align:center">2016-07-31</td> <td style="text-align:center">$1,632</td> </tr> <tr> <td style="text-align:center">2016-04-30</td> <td style="text-align:center">$1,972</td> </tr> <tr> <td style="text-align:center">2016-01-31</td> <td style="text-align:center">$3,525</td> </tr> <tr> <td style="text-align:center">2015-10-31</td> <td style="text-align:center">$2,016</td> </tr> <tr> <td style="text-align:center">2015-07-31</td> <td style="text-align:center">$1,762</td> </tr> <tr> <td style="text-align:center">2015-04-30</td> <td style="text-align:center">$2,061</td> </tr> <tr> <td style="text-align:center">2015-01-31</td> <td style="text-align:center">$3,476</td> </tr> <tr> <td style="text-align:center">2014-10-31</td> <td style="text-align:center">$2,092</td> </tr> <tr> <td style="text-align:center">2014-07-31</td> <td style="text-align:center">$1,731</td> </tr> <tr> <td style="text-align:center">2014-04-30</td> <td style="text-align:center">$1,996</td> </tr> <tr> <td style="text-align:center">2014-01-31</td> <td style="text-align:center">$3,684</td> </tr> <tr> <td style="text-align:center">2013-10-31</td> <td style="text-align:center">$2,107</td> </tr> <tr> <td style="text-align:center">2013-07-31</td> <td style="text-align:center">$1,384</td> </tr> <tr> <td style="text-align:center">2013-04-30</td> <td style="text-align:center">$1,865</td> </tr> <tr> <td style="text-align:center">2013-01-31</td> <td style="text-align:center">$3,562</td> </tr> <tr> <td style="text-align:center">2012-10-31</td> <td style="text-align:center">$1,773</td> </tr> <tr> <td style="text-align:center">2012-07-31</td> <td style="text-align:center">$1,550</td> </tr> <tr> <td style="text-align:center">2012-04-30</td> <td style="text-align:center">$2,002</td> </tr> <tr> <td style="text-align:center">2012-01-31</td> <td style="text-align:center">$3,579</td> </tr> <tr> <td style="text-align:center">2011-10-31</td> <td style="text-align:center">$1,947</td> </tr> <tr> <td style="text-align:center">2011-07-31</td> <td style="text-align:center">$1,744</td> </tr> <tr> <td style="text-align:center">2011-04-30</td> <td style="text-align:center">$2,281</td> </tr> <tr> <td style="text-align:center">2011-01-31</td> <td style="text-align:center">$3,693</td> </tr> <tr> <td style="text-align:center">2010-10-31</td> <td style="text-align:center">$1,899</td> </tr> <tr> <td style="text-align:center">2010-07-31</td> <td style="text-align:center">$1,799</td> </tr> <tr> <td style="text-align:center">2010-04-30</td> <td style="text-align:center">$2,083</td> </tr> <tr> <td style="text-align:center">2010-01-31</td> <td style="text-align:center">$3,524</td> </tr> <tr> <td style="text-align:center">2009-10-31</td> <td style="text-align:center">$1,835</td> </tr> <tr> <td style="text-align:center">2009-07-31</td> <td style="text-align:center">$1,739</td> </tr> <tr> <td style="text-align:center">2009-04-30</td> <td style="text-align:center">$1,981</td> </tr> <tr> <td style="text-align:center">2009-01-31</td> <td style="text-align:center">$3,492</td> </tr> <tr> <td style="text-align:center">2008-10-31</td> <td style="text-align:center">$1,696</td> </tr> <tr> <td style="text-align:center">2008-07-31</td> <td style="text-align:center">$1,804</td> </tr> <tr> <td style="text-align:center">2008-04-30</td> <td style="text-align:center">$1,814</td> </tr> <tr> <td style="text-align:center">2008-01-31</td> <td style="text-align:center">$2,866</td> </tr> <tr> <td style="text-align:center">2007-10-31</td> <td style="text-align:center">$1,611</td> </tr> <tr> <td style="text-align:center">2007-07-31</td> <td style="text-align:center">$1,338</td> </tr> <tr> <td style="text-align:center">2007-04-30</td> <td style="text-align:center">$1,279</td> </tr> <tr> <td style="text-align:center">2007-01-31</td> <td style="text-align:center">$2,304</td> </tr> <tr> <td style="text-align:center">2006-10-31</td> <td style="text-align:center">$1,012</td> </tr> <tr> <td style="text-align:center">2006-07-31</td> <td style="text-align:center">$963</td> </tr> <tr> <td style="text-align:center">2006-04-30</td> <td style="text-align:center">$1,040</td> </tr> <tr> <td style="text-align:center">2006-01-31</td> <td style="text-align:center">$1,667</td> </tr> <tr> <td style="text-align:center">2005-10-31</td> <td style="text-align:center">$534</td> </tr> <tr> <td style="text-align:center">2005-07-31</td> <td style="text-align:center">$416</td> </tr> <tr> <td style="text-align:center">2005-04-30</td> <td style="text-align:center">$475</td> </tr> <tr> <td style="text-align:center">2005-01-31</td> <td style="text-align:center">$709</td> </tr> </tbody>
In [73]:
for row in table.find_all('tr'):
columns = row.find_all('td')
if len(columns) > 0:
date = columns[0].text.strip()
revenue = columns[1].text.strip()
n_row = pd.DataFrame({'Date': [date], 'Revenue': [revenue]})
gme_revenue = pd.concat([gme_revenue, n_row], ignore_index=True)
gme_revenue['Revenue'] = gme_revenue['Revenue'].replace('[\$,]', '', regex=True)
gme_revenue = gme_revenue[gme_revenue['Revenue'].str.strip() != '']
gme_revenue = gme_revenue.dropna(subset=['Revenue'])
gme_revenue['Revenue'] = gme_revenue['Revenue'].astype(float)
print(gme_revenue.head())
Date Revenue 0 2020-04-30 1021.0 1 2020-01-31 2194.0 2 2019-10-31 1439.0 3 2019-07-31 1286.0 4 2019-04-30 1548.0
Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.
In [74]:
gme_revenue.tail()
Out[74]:
| Date | Revenue | |
|---|---|---|
| 57 | 2006-01-31 | 1667.0 |
| 58 | 2005-10-31 | 534.0 |
| 59 | 2005-07-31 | 416.0 |
| 60 | 2005-04-30 | 475.0 |
| 61 | 2005-01-31 | 709.0 |
Question 5: Plot Tesla Stock Graph¶
Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. Note the graph will only show data upto June 2021.
In [75]:
make_graph(tesla_data, tesla_revenue, "Tesla")
/tmp/ipykernel_4257/109047474.py:5: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument. /tmp/ipykernel_4257/109047474.py:6: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
In [ ]:
Question 6: Plot GameStop Stock Graph¶
Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.
Hint
You just need to invoke the make_graph function with the required parameter to print the graphs.The structure to call the `make_graph` function is `make_graph(gme_data, gme_revenue, 'GameStop')`
In [76]:
make_graph(gme_data, gme_revenue, "GameStop")
/tmp/ipykernel_4257/109047474.py:5: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument. /tmp/ipykernel_4257/109047474.py:6: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
In [80]:
!jupyter nbconvert --to html YourNotebook.ipynb
[NbConvertApp] WARNING | pattern 'YourNotebook.ipynb' matched no files
This application is used to convert notebook files (*.ipynb)
to various other formats.
WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.
Options
=======
The options below are convenience aliases to configurable class-options,
as listed in the "Equivalent to" description-line of the aliases.
To see all configurable class-options for some <cmd>, use:
<cmd> --help-all
--debug
set log level to logging.DEBUG (maximize logging output)
Equivalent to: [--Application.log_level=10]
--show-config
Show the application's configuration (human-readable format)
Equivalent to: [--Application.show_config=True]
--show-config-json
Show the application's configuration (json format)
Equivalent to: [--Application.show_config_json=True]
--generate-config
generate default config file
Equivalent to: [--JupyterApp.generate_config=True]
-y
Answer yes to any questions instead of prompting.
Equivalent to: [--JupyterApp.answer_yes=True]
--execute
Execute the notebook prior to export.
Equivalent to: [--ExecutePreprocessor.enabled=True]
--allow-errors
Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.
Equivalent to: [--ExecutePreprocessor.allow_errors=True]
--stdin
read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'
Equivalent to: [--NbConvertApp.from_stdin=True]
--stdout
Write notebook output to stdout instead of files.
Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]
--inplace
Run nbconvert in place, overwriting the existing notebook (only
relevant when converting to notebook format)
Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]
--clear-output
Clear output of current file and save in place,
overwriting the existing notebook.
Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]
--coalesce-streams
Coalesce consecutive stdout and stderr outputs into one stream (within each cell).
Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --CoalesceStreamsPreprocessor.enabled=True]
--no-prompt
Exclude input and output prompts from converted document.
Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]
--no-input
Exclude input cells and output prompts from converted document.
This mode is ideal for generating code-free reports.
Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True --TemplateExporter.exclude_input_prompt=True]
--allow-chromium-download
Whether to allow downloading chromium if no suitable version is found on the system.
Equivalent to: [--WebPDFExporter.allow_chromium_download=True]
--disable-chromium-sandbox
Disable chromium security sandbox when converting to PDF..
Equivalent to: [--WebPDFExporter.disable_sandbox=True]
--show-input
Shows code input. This flag is only useful for dejavu users.
Equivalent to: [--TemplateExporter.exclude_input=False]
--embed-images
Embed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.
Equivalent to: [--HTMLExporter.embed_images=True]
--sanitize-html
Whether the HTML in Markdown cells and cell outputs should be sanitized..
Equivalent to: [--HTMLExporter.sanitize_html=True]
--log-level=<Enum>
Set the log level by value or name.
Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
Default: 30
Equivalent to: [--Application.log_level]
--config=<Unicode>
Full path of a config file.
Default: ''
Equivalent to: [--JupyterApp.config_file]
--to=<Unicode>
The export format to be used, either one of the built-in formats
['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'qtpdf', 'qtpng', 'rst', 'script', 'slides', 'webpdf']
or a dotted object name that represents the import path for an
``Exporter`` class
Default: ''
Equivalent to: [--NbConvertApp.export_format]
--template=<Unicode>
Name of the template to use
Default: ''
Equivalent to: [--TemplateExporter.template_name]
--template-file=<Unicode>
Name of the template file to use
Default: None
Equivalent to: [--TemplateExporter.template_file]
--theme=<Unicode>
Template specific theme(e.g. the name of a JupyterLab CSS theme distributed
as prebuilt extension for the lab template)
Default: 'light'
Equivalent to: [--HTMLExporter.theme]
--sanitize_html=<Bool>
Whether the HTML in Markdown cells and cell outputs should be sanitized.This
should be set to True by nbviewer or similar tools.
Default: False
Equivalent to: [--HTMLExporter.sanitize_html]
--writer=<DottedObjectName>
Writer class used to write the
results of the conversion
Default: 'FilesWriter'
Equivalent to: [--NbConvertApp.writer_class]
--post=<DottedOrNone>
PostProcessor class used to write the
results of the conversion
Default: ''
Equivalent to: [--NbConvertApp.postprocessor_class]
--output=<Unicode>
Overwrite base name use for output files.
Supports pattern replacements '{notebook_name}'.
Default: '{notebook_name}'
Equivalent to: [--NbConvertApp.output_base]
--output-dir=<Unicode>
Directory to write output(s) to. Defaults
to output to the directory of each notebook. To recover
previous default behaviour (outputting to the current
working directory) use . as the flag value.
Default: ''
Equivalent to: [--FilesWriter.build_directory]
--reveal-prefix=<Unicode>
The URL prefix for reveal.js (version 3.x).
This defaults to the reveal CDN, but can be any url pointing to a copy
of reveal.js.
For speaker notes to work, this must be a relative path to a local
copy of reveal.js: e.g., "reveal.js".
If a relative path is given, it must be a subdirectory of the
current directory (from which the server is run).
See the usage documentation
(https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-slideshow)
for more details.
Default: ''
Equivalent to: [--SlidesExporter.reveal_url_prefix]
--nbformat=<Enum>
The nbformat version to write.
Use this to downgrade notebooks.
Choices: any of [1, 2, 3, 4]
Default: 4
Equivalent to: [--NotebookExporter.nbformat_version]
Examples
--------
The simplest way to use nbconvert is
> jupyter nbconvert mynotebook.ipynb --to html
Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'qtpdf', 'qtpng', 'rst', 'script', 'slides', 'webpdf'].
> jupyter nbconvert --to latex mynotebook.ipynb
Both HTML and LaTeX support multiple output templates. LaTeX includes
'base', 'article' and 'report'. HTML includes 'basic', 'lab' and
'classic'. You can specify the flavor of the format used.
> jupyter nbconvert --to html --template lab mynotebook.ipynb
You can also pipe the output to stdout, rather than a file
> jupyter nbconvert mynotebook.ipynb --stdout
PDF is generated via latex
> jupyter nbconvert mynotebook.ipynb --to pdf
You can get (and serve) a Reveal.js-powered slideshow
> jupyter nbconvert myslides.ipynb --to slides --post serve
Multiple notebooks can be given at the command line in a couple of
different ways:
> jupyter nbconvert notebook*.ipynb
> jupyter nbconvert notebook1.ipynb notebook2.ipynb
or you can specify the notebooks list in a config file, containing::
c.NbConvertApp.notebooks = ["my_notebook.ipynb"]
> jupyter nbconvert --config mycfg.py
To see all available configurables, use `--help-all`.
About the Authors:
Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.
Azim Hirjani
Change Log¶
| Date (YYYY-MM-DD) | Version | Changed By | Change Description |
|---|---|---|---|
| 2022-02-28 | 1.2 | Lakshmi Holla | Changed the URL of GameStop |
| 2020-11-10 | 1.1 | Malika Singla | Deleted the Optional part |
| 2020-08-27 | 1.0 | Malika Singla | Added lab to GitLab |
© IBM Corporation 2020. All rights reserved.